Internal knowledge search agent
Searches documents, meeting notes, manuals, and policies, then answers with supporting context.
Services / 03
AI Agent Development & Operations
We design, build, and operate AI agents that fit real business workflows, from PoC to production operations.
Atlas Support designs and builds AI agents that connect to internal knowledge, business systems, SaaS tools, databases, and workflow processes.
This is not limited to chatbot setup or prompt writing. We define the workflow, RAG, API integrations, human review points, permissions, logs, and evaluation measures needed for practical AI agent implementation.
When a theme has been validated through AI Advisory, we can move it into a scoped PoC, MVP, or production operation step by step.
- RAG / Internal Knowledge Search
- API / SaaS Integrations
- Workflow Automation
- Human-in-the-loop
- Permissions, Logs, and Audit Design
- PoC to Production Operations
Buildable Agents
Examples of AI agents we can build
We build AI agents that search for information, support decisions, and operate external tools where the workflow and review rules are clear.
Inquiry response agent
References FAQs, past inquiries, and customer context to draft responses and hand off to a human reviewer.
Sales support agent
Uses CRM records, deal notes, and proposal material to organize next actions, proposal outlines, and customer-specific issues.
Back-office operations agent
Checks requests against policies and prior cases, then prepares review points or return comments.
Research and reporting agent
Uses web information, internal documents, and data to prepare research notes or executive summaries.
Development and code investigation agent
Reads issues, code, README files, and specifications to organize implementation options and test viewpoints.
Workflow execution agent
Connects with Slack, Google Workspace, Notion, CRM, spreadsheets, and similar tools to support recurring operations.
Multi-agent workflow system
Coordinates agents for research, review, summarization, approval support, and other role-based workflows.
Design Elements
What practical AI agents need beyond prompts
AI agents are not finished by prompt design alone. Business use requires data, tools, permissions, human review, logs, and evaluation design.
LLM design
Model selection and prompt design across OpenAI, Claude, Gemini, OSS models, and other options based on the use case.
RAG / knowledge base
Organizing documents, FAQs, meeting notes, policies, and manuals into searchable knowledge foundations.
Tool and API integration
Connecting CRM, SFA, Google Workspace, Slack, Notion, databases, internal systems, and external APIs.
Workflow design
Defining what the agent can handle, what people review, how exceptions are routed, and where approvals happen.
Permission design
Clarifying which data can be viewed and which actions can be taken by department, role, and workflow.
Human-in-the-loop
Placing human review and approval before sensitive outputs or actions.
Logging and auditability
Recording sources, outputs, tool calls, and action history so later review is possible.
Evaluation and improvement
Improving with answer quality, saved effort, usage, error rates, and review workload as practical measures.
Process
From PoC to production operations
AI agent development starts with a focused workflow, validates fit through a lightweight PoC, and then moves toward production use only after the evidence is clear.
Clarify the workflow theme
Define the target workflow, current process, users, input data, outputs, and success conditions. Themes validated through AI Advisory can be connected into a development project.
Requirements and design
Define agent scope, human review scope, required data, tool integrations, permissions, logs, and exception handling.
Prototype / PoC
Build a lightweight prototype with RAG, API integration, workflow logic, and UI for a limited business scope. Validate output quality, workflow fit, and operational risk.
MVP development
For validated themes, implement usable screens, integrations, permissions, logs, and management functions.
Production implementation
Prepare the environment needed for real use across existing systems, cloud infrastructure, authentication, and internal operating rules.
Operations and improvement
Use logs, field feedback, and KPIs to improve prompts, search quality, workflow logic, UI, and permission settings.
Deliverables
What an AI agent development project can produce
Depending on the phase, deliverables can include design documents, prototypes, applications, operating procedures, and improvement reports.
| Phase | Main deliverables |
|---|---|
| Requirements | Business requirements, target workflow, agent scope, human review design |
| Design | System architecture, RAG design, API integration design, permission design, logging design |
| PoC | Simple demo, prototype, validation report, list of improvement issues |
| MVP development | Usable application, admin screens, integrations, test results |
| Production rollout | Production environment, operating procedure, usage guide, risk and governance notes |
| Operations improvement | Usage log analysis, KPI report, improvement proposals, prompt and search quality updates |
Deliverables are adjusted to the project. We define the necessary scope in advance and proceed by PoC, MVP, production development, and operations improvement phase.
Use Themes
Business workflows that can become AI agent projects
Inquiry handling
Supports response drafting, source presentation, and handoff by referencing internal FAQs, customer inquiries, and product manuals.
Internal knowledge search
Searches documents, meeting notes, policies, and manuals across teams, with answers adapted to roles and departments.
Sales support
Uses deal history, CRM, and proposal material to prepare next actions, proposal outlines, customer risks, and email drafts.
Back-office review
Supports expense review, contract checks, approval requests, and policy checks by preparing review points and return comments.
Research and planning
Supports market research, competitor comparison, internal data analysis, and executive summary preparation.
Development support
Reads codebases, issues, specifications, and README files to organize investigation, implementation options, and test viewpoints.
Advisory vs Development
How AI Advisory differs from AI Agent Development
| Item | AI Advisory & Agent Strategy | AI Agent Development & Operations |
|---|---|---|
| Purpose | Validate the theme before development | Implement a validated theme |
| Main work | Research, design, lightweight validation, PoC planning | Requirements, design, development, production rollout, operations improvement |
| Deliverables | Reports, design notes, simple demos, PoC plans | Systems, applications, RAG foundations, integrations, operations design |
| Format | Monthly advisory | Project-based development |
| Best fit | You need to decide what should be built | The workflow theme is already reasonably clear |
If the theme is still unclear, AI Advisory validates one AI theme at a time. If the workflow, required data, and investment decision are clearer, AI Agent Development can move into PoC or production implementation.
Validate the theme through AI AdvisoryScope
We define scope before development begins
AI agents need to be designed around workflow, data, permissions, and human review. Atlas Support clarifies the operating scope before implementation so the agent can be used safely in real work.
Scope we can cover
- Business requirements definition
- AI agent design
- RAG / internal knowledge search development
- Prompt design
- API / SaaS integration
- Workflow automation
- Human-in-the-loop design
- Permission, logging, and audit design
- PoC / MVP development
- Production rollout support
- Operations improvement
Not included by default
- Large-scale development while specifications remain undefined
- High-risk work without human review or approval
- Replacing specialist judgment in medical, legal, financial, or regulated decisions
- Large-scale core system replacement
- Large projects focused only on data cleansing
- 24/7 staffed operations monitoring
- Commitments that promise a specific reduction result
- Large-scale modification of existing systems
- Security audits or legal review itself
For high-risk judgments or actions with external impact, we design around human confirmation and approval. Permissions, logs, audits, and exception handling are part of putting AI agents into business operations safely.
FAQ
Questions about AI Agent Development & Operations
How is AI agent development different from chatbot development?
A chatbot mainly answers questions. An AI agent can reference internal data and external tools, move through multiple workflow steps, draft outputs, hand work to a human reviewer, and leave logs for later review.
Can you integrate with existing systems and SaaS tools?
Yes. We can design integrations with CRM, SFA, Google Workspace, Slack, Notion, databases, internal APIs, and other systems. Feasibility depends on API specifications, authentication, and permission design.
Can you support RAG and internal knowledge search?
Yes. We can design AI agents that search internal documents, FAQs, manuals, meeting notes, and policies, then answer with supporting context.
Can we request PoC only?
Yes. A focused PoC can validate workflow fit, output quality, required data, and operational risks before deciding whether to move into MVP or production development.
How is this different from AI Advisory?
AI Advisory validates one AI theme at a time through research, design, light demos, and PoC planning. AI Agent Development is a project-based service that implements a validated theme as a PoC, MVP, or production operation.
Can you improve the system after production use?
Yes. We can improve prompts, RAG, UI, workflows, and permission settings using logs, answer quality, usage data, and field feedback.
Can an AI agent run business work without human review?
For high-risk decisions or actions with external impact, we design around human review and approval. A practical starting point is usually a semi-autonomous agent that assists human judgment.
Don’t let AI agent development stop at the PoC stage.
AI agent development requires clear theme selection and workflow design before full-scale implementation. If you already have a workflow to build, contact us about PoC or development support. If the theme is still unclear, AI Advisory can help validate one AI theme at a time before moving into development.